With the recent rapid growth of the wireless telecommunication market, there has been a demand for a variety of multimedia services in a wireless environment. In particular, the size of data to be transmitted and the speed of data transmission are increasing. Therefore, there is an urgent need for a method of effectively using limited wireless resources. To address this issue, a new transmission technique using multiple antennas is required. By way of example, a multiple-input, multiple-output (MIMO) technique using multiple antennas is taken into account. According to the MIMO technique, both a transmission end and a receiving end use multiple antennas. In comparison with a system using a single antenna, the MIMO technique has an advantage in that a channel transmission capacity can increase in proportion to the number of antennas without having to allocate additional frequencies or transmission power. For this reason, the MIMO technique is recently regarded as a remarkable communication technique.
The MIMO technique is classified into spatial diversity scheme in which transmission reliability increases by obtaining a diversity gain corresponding to the product of a pair of transmit (Tx) and receive (Rx) antennas, a spatial multiplexing (SM) scheme in which a data transfer rate increases by simultaneously transmitting a plurality of signal streams, and a scheme in which the spatial diversity and the SM are combined.
When using the SM scheme, the transmitting end simultaneously transmits different pieces of information respectively through a plurality of transmit (Tx) antennas, thereby enabling fast data transmission. In this case, the different pieces of information are simultaneously transmitted by using the plurality of Tx antennas, and thus receive (Rx) antennas of the receiving end receive signals in which all transmit (Tx) signals are combined. Therefore, such multiplexed signals have to be de-multiplexed for the respective antennas in the receiving end. Examples of a method of detecting a signal for each antenna in a receiving end of a system using the SM scheme include a Zero Forcing (ZF) method, a Minimum Mean Square Error (MMSE) method, and an Order Successive Interference Cancellation (OSIC) method.
As a linear signal detection method, the ZF method and the MMSE method can be implemented with a relatively simple structure due to a low operational complexity, but provide poor throughput. When using the OSIC method, signals are sequentially detected according to a predetermined detection order, and these signals are removed from Rx signals. In comparison with the linear signal detection method, the OSIC method has a significant operational complexity, but shows superior throughput to the linear signal detection method. However, when compared with a Maximum Likelihood (ML) scheme showing the most optimal throughput, the OSIC method has a relatively low throughput.
In the ML scheme, all transmittable signal vectors are taken into account and thus a signal having the shortest Euclidean distance with respect to an Rx signal is selected. The ML scheme is an optimal method which is used as a reference when throughput is compared with other methods. However, an operational complexity exponentially increases in proportion to the number of Tx antennas and a modulation order. Therefore, it is difficult for a system to use the ML scheme in practice.
The signal detection method may be sphere decoding which shows the same throughput as the ML scheme. However, the sphere decoding cannot be easily implemented since it is difficult to obtain a radius of an initial sphere and an operational complexity required in the worst situation is very high. A QR Decomposition-M (QRD-M) scheme may also be used but has a demerit in that throughput may significantly vary depending on the number of candidate groups. The QRD-M scheme shows almost the same throughput as the ML scheme when the number of candidate groups is enough. However, the lesser the number of the candidate groups, the greater the deterioration in throughput. In addition, an operational complexity increases in proportion to the number of candidate groups.
In an SM-type receiver, it is known that superior throughput can be obtained when decoding is performed by delivering a soft decision value to a channel decoder instead of delivering a hard decision value of a coded bit to the channel decoder. The soft decision value input to the decoder uses a Log Likelihood Ratio (LLR) value as an estimation value of a modulation symbol that is transmitted through a channel. Therefore, the SM-type receiver requires not only a first algorithm for receiving signals with low complexity but also a second algorithm for generating an optimal LLR by using the first algorithm
In general, when using the linear signal detection method (i.e., ZF and MMSE) and the non-linear signal detection method (i.e., OSIC), an operation for computing a square Euclidean distance is performed to generate an LLR. In addition, when using the sphere decoding, sufficient throughput improvement is achieved when many candidate groups of detection signals are provided. In this case, an operational complexity is great because the square Euclidean distance has to be obtained for each candidate group. In the case of using the QRD-M scheme, each bit has a probability value of 0 or 1 when an LLR is generated. However, such probability value cannot be obtained when a specific bit does not exist in a candidate group.
As such, many problems exist in the aforementioned signal detection methods proposed for the SM scheme. Therefore, there is a need for a method in which an LLR used in soft decision of a decoder is effectively generated and which has a low operational complexity and a throughput similar to that of the conventional ML scheme showing an optimal throughput.